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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 1,621 records · Page 90
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Microbial community response to a decade of simulated global changes depends on the plant community

Global changes such as increased drought and atmospheric nitrogen deposition perturb both the microbial and plant communities that mediate terrestrial ecosystem functioning. However, few studies consider how microbial responses to global changes may be influenced by interactions with plant communities. To begin to address the role of microbial–plant interactions, we tested the hypothesis that the response of microbial communities to global change depends on the plant community. We characterized bacterial and fungal communities from 395 plant litter samples taken from the Loma Ridge Global Change Experiment, a decade-long global change experiment in Southern California that manipulates rainfall and nitrogen levels across two adjacent ecosystems, a grassland and a coastal sage scrubland. The differences in bacterial and fungal composition between ecosystems paralleled distinctions in plant community composition. In addition to the direct main effects, the global change treatments altered microbial composition in an ecosystem-dependent manner, in support of our hypothesis. The interaction between the drought treatment and ecosystem explained nearly 5% of the variation in bacterial community composition, similar to the variation explained by the ecosystem-independent effects of drought. Unexpectedly, we found that the main effect of drought was approximately four times as strong on bacterial composition as that of nitrogen addition, which did not alter fungal or plant composition. Overall, the findings underscore the importance of considering plant–microbe interactions when considering the transferability of the results of global change experiments across ecosystems.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Analysis of Waste Material Feedstocks Using Laser-Induced Breakdown Spectroscopy and Machine Learning

Predicting properties such as heating value, ash fusion temperature, and mineral ash composition from Laser-Induced Breakdown Spectroscopy (LIBS) data can make gasifiers more flexible to different feedstocks. Understanding these feedstock properties in-situ improves feedstock conversion modelling methods that allow for consistent operation, higher carbon conversion, and reduced fouling and erosion rates. The purpose of this study is to demonstrate methods for model creation that take LIBS data as predictor features and estimate higher order material properties as a function of feedstock material properties. Six samples were chosen to represent a mixture of abundant and carbon rich waste materials. LIBS measurements were performed on these samples for elemental wavelengths and intensity values. Laboratory analytical results were obtained for each sample’s heating value, proximate and ultimate analysis, mineral ash composition, ash fusion temperatures, and viscosity temperatures. Thermal conductivity was measured using a HotDisk TPS 2500S. LIBS measurements were processed and used as predictor features for machine learning (ML) models to predict the sample’s material properties. Predictor feature selection algorithms, particularly minimum redundancy maximum relevance (mRMR), reduced the dimensionality of ML models. Many modelling methods such as Gaussian process regression (GPR), regression tree, neural networks (NN), and support vector machines (SVM) were demonstrated to be effective at predicting higher order properties; however, mRMR with GPR stood out as a clear winning combination.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2022

In vivo Functional Characterization of Hydrophilic X2 Modules in the Cellulosomal Scaffolding Protein

As part of free cellulases or scaffolding proteins in cellulosomes, the hydrophilic non-catalytic X2 module is widely distributed in cellulolytic Clostridia or other Firmicutes bacteria. Previous biochemical studies suggest that X2 modules might increase the solubility and substrate binding affinity of X2-bearing proteins. However, their in vivo biological functions remain elusive. Here we employed CRISPR-Cas9 editing to genetically modify X2 modules by deleting the conserved motif (NGNT) from the CipC scaffoldin. Both single and double X2 mutants (X2-N: near the N terminus of CipC; X2-C: near the C terminus of CipC) presented similar stoichiometric compositions in isolated cellulosomes as the wildtype strain (WT). These X2 mutants had an elongated adaptation stage during growth on cellulose compared to cellobiose. Compared to WT, the double mutant ΔX2-NC reduced cellulose degradation by 15% and the amount of released soluble sugars by 63%. Since single X2 mutants did not present such obvious physiological changes as ΔX2-NC, there seems to be a functional redundancy between X2 modules in CipC. The in vivo adhesion assay revealed that ΔX2-NC decreased cell attachment to cellulose by 70% but a weaker effect was also overserved in single X2 mutants. These results highlight the in vivo biological role of X2 in increasing cellulose degradation efficiency by enhancing the binding affinity between cells and cellulose, which provides new perspectives for microbial engineering.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Functional and transcriptional characterization of complex neuronal co-cultures

Brain-on-a-chip systems are designed to simulate brain activity using traditional in vitro cell culture on an engineered platform. It is a noninvasive tool to screen new drugs, evaluate toxicants, and elucidate disease mechanisms. However, successful recapitulation of brain function on these systems is dependent on the complexity of the cell culture. In this study, we increased cellular complexity of traditional (simple) neuronal cultures by co-culturing with astrocytes and oligodendrocyte precursor cells (complex culture). We evaluated and compared neuronal activity (e.g., network formation and maturation), cellular composition in long-term culture, and the transcriptome of the two cultures. Compared to simple cultures, neurons from complex co-cultures exhibited earlier synapse and network development and maturation, which was supported by localized synaptophysin expression, up-regulation of genes involved in mature neuronal processes, and synchronized neural network activity. Also, mature oligodendrocytes and reactive astrocytes were only detected in complex cultures upon transcriptomic analysis of age-matched cultures. Functionally, the GABA antagonist bicuculline had a greater influence on bursting activity in complex versus simple cultures. Collectively, the cellular complexity of brain-on-a-chip systems intrinsically develops cell type-specific phenotypes relevant to the brain while accelerating the maturation of neuronal networks, important features underdeveloped in traditional cultures.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Elucidating Primary Degradation Mechanisms in High-Cycling-Capacity, Compositionally Tunable High-Entropy Hydrides

The hydrogen sorption properties of single-phase bcc (TiVNb) 100–x Cr x alloys (x = 0–35) are reported. All alloys absorb hydrogen quickly at 25 °C, forming fcc hydrides with storage capacity depending on the Cr content. Here, a thermodynamic destabilization of the fcc hydride is observed with increasing Cr concentration, which agrees well with previous compositional machine learning models for metal hydride thermodynamics. The steric effect or repulsive interactions between Cr–H might be responsible for this behavior. The cycling performances of the TiVNbCr alloy show an initial decrease in capacity, which cannot be explained by a structural change. Pair distribution function analysis of the total X-ray scattering on the first and last cycled hydrides demonstrated an average random fcc structure without lattice distortion at short-range order. If the as-cast alloy contains a very low density of defects, the first hydrogen absorption introduces dislocations and vacancies that cumulate into small vacancy clusters, as revealed by positron annihilation spectroscopy. Finally, the main reason for the capacity drop seems to be due to dislocations formed during cycling, while the presence of vacancy clusters might be related to the lattice relaxation. Having identified the major contribution to the capacity loss, compositional modifications to the TiVNbCr system can now be explored that minimize defect formation and maximize material cycling performance.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Reduction of CO 2 Emissions Through Lightweight Body Panels (Project Final Report)

Lightweight construction is an integral part of Volkswagen’s overall strategy of reducing CO 2 emissions. Due to its low cost, steel is the most commonly used material for automotive exterior body panels today. Unfortunately, steel has a high density, resulting in a relatively low specific strength. Glass fiber based sheet molding compounds (SMC) provide high properties in combination with lower density. The high specific strength of SMC offers an enormous lightweight potential. To unlock the full potential of SMC materials in combination with cutting edge manufacturing processes, Volkswagen Group of America worked together with IACMI (Institute for Advanced Composites Manufacturing Innovation) and the academic partners: University of Tennessee Knoxville, Purdue University and Michigan State University; as well as the industry partners: Ashland, IDI, Owens Corning and Continental Structural Plastics. Leveraging the expertise of all project partners, reaching over the entire supply chain the project demonstrated the potential of these materials. This report will highlight the major steps in the development process on the way to technology readiness for SMC using the example of the Volkswagen Atlas Liftgate. Over the 36-month period of the project, the work focused on three R&D areas: material development, design and simulation, and development of the manufacturing process. Material selection included alternative fibers and resin systems, and accounted for material availability, properties, and cost-efficiency. The work undertaken in the field of design and simulation has pushed the envelope of short fiber reinforced thermoset molding compound process simulation. Design iterations were virtually tested, while the final design was used to validate the simulation software against physical parts. Manufacturing development used cutting-edge technology, while experts along the supply chain were working together to ensure the best possible results. In the final stage of the project, liftgates were molded, trimmed, bonded, painted and assembled before exhaustive testing. The result is an e-coat (electrophoretic dip coating) capable Class-A SMC liftgate, which is ready for high-volume production, and can be used as a technology demonstrator. The prototypes manufactured in the scope of this work have exhibited a mass reduction for the Volkswagen Atlas liftgate of up to 35% compared to the series production steel version, without a degradation of the functionalities.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Direct aromatization of CO 2 via combined CO 2 hydrogenation and zeolite-based acid catalysis

Aromatics, including benzene, toluene, and xylenes (BTX), are essential chemical building blocks and are widely used as solvents, fuel additives, and polymers. With the recent development in CO 2 capture technologies and the progress made in producing H 2 using renewable energy, direct hydrogenation of CO 2 to aromatics via heterogeneous catalysis has emerged as a promising pathway to accomplish the production of aromatics with simultaneous utilization of waste CO 2 . In this review, we focus on recent advances in the nascent field of direct CO 2 aromatization, whereby tandem catalysts composed of CO 2 hydrogenation and aromatization functionalities are designed and deployed. We review two categories of tandem catalysts: catalysts integrating Fe-based/H-ZSM-5 components following RWGS (reverse water-gas shift of CO 2 to CO)-FT (Fischer-Tropsch synthesis of lower olefins)-aromatization pathways, and catalysts combining metal oxide/H-ZSM-5 domains following CO 2 to methanolaromatization pathways. The key parameters that determine the catalytic performance, such as the composition and structure of the Fe-based or metal oxide-based CO 2 conversion catalysts, the properties of H-ZSM-5, and the synergy between the two components, are analyzed to provide insights for the design of efficient tandem catalysts for CO 2 aromatization. In parallel, thermodynamic analyses, mechanistic studies, and density functional theory (DFT) computations for the relevant reaction routes and pathways are discussed to offer improved understanding of CO 2 activation, reaction intermediates, and product formation. In closing, the challenges and prospects for these tandem reactions are addressed to provide suggested paths forward for future research.

10 SYNTHETIC FUELS↗
DOE OSTI2021

Algorithm-Driven Advances for Scientific CT Instruments: From model-based to deep learning-based approaches

Multiscale 3D characterization is widely used by materials scientists to further their understanding of the relationships between microscopic structure and macroscopic function. Scientific computed tomography (SCT) instruments are one of the most popular choices for 3D nondestructive characterization of materials at length scales ranging from the angstrom scale to the micron scale. These instruments typically have a source of radiation (such as electrons, X-rays, or neutrons) that interacts with the sample to be studied and a detector assembly to capture the result of this interaction (see Figure 1 ). A collection of such high-resolution measurements is made by reorienting the sample, which is mounted on a specially designed stage/holder after which reconstruction algorithms are used to produce the final 3D volume of interest. The specific choice of which instrument to use depends on the desired resolution and properties of the materials being imaged. Additionally, the end goal of SCT scans includes determining the morphology, chemical composition, or dynamic behavior of materials when subjected to external stimuli. In summary, SCT instruments are powerful tools that enable 3D characterization across multiple length scales and play a critical role in furthering the understanding of the structure–function relationships of different materials.

42 ENGINEERING↗
DOE OSTI2021

Recent advances in ink-based additive manufacturing for porous structures

The use of porous structures is an ancient wisdom, people found its importance ever since we began to understand nature. The evolution of porous structure gives rise to multi-scale (combined nano-, micro-, and macro-) porous networks. Traditional manufacturing technologies have challenges in creating shape-complex and conformable porous structures for real-world applications. Additive manufacturing (AM) with the capability of assembling various materials with complex and customized architectures is rapidly developing. With the advancement of material development, the integration of AM and porous structure offers unparalleled and emerging opportunities for concept-to-design-to-fabrication of multi-scale porous networks, enabling the multi-functionalities of the 3D printed specimen and broadening its applications. In this work, we provide a comprehensive review of the state-of-the-art ink-based AM techniques for 3D printing porous structures. The ink design principle, ink composition, and printability are thoroughly discussed. The methodologies of various AM technologies for porous structures are analyzed. Readers can find a clear experimental guidance toward 3D printing multi-scale porous structures. The synergistic and collective merits of additive manufacturing and porous structure are highlighted and systematically discussed in this review. The challenges and promises of this field for future research are also outlined.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Structure-based decoupling of the pro- and anti-inflammatory functions of interleukin-10

Interleukin-10 (IL-10) is an immunoregulatory cytokine with both anti-inflammatory and immunostimulatory properties and is frequently dysregulated in disease. We used a structure-based approach to deconvolute IL-10 pleiotropy by determining the structure of the IL-10 receptor (IL-10R) complex by cryo–electron microscopy at a resolution of 3.5 angstroms. The hexameric structure shows how IL-10 and IL-10Rα form a composite surface to engage the shared signaling receptor IL-10Rβ, enabling the design of partial agonists. IL-10 variants with a range of IL-10Rβ binding strengths uncovered substantial differences in response thresholds across immune cell populations, providing a means of manipulating IL-10 cell type selectivity. Some variants displayed myeloid-biased activity by suppressing macrophage activation without stimulating inflammatory CD8 + T cells, thereby uncoupling the major opposing functions of IL-10. These results provide a mechanistic blueprint for tuning the pleiotropic actions of IL-10.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Multisublattice cluster expansion study of short-range ordering in iron-substituted strontium titanate

Owing to the challenges in obtaining realistic atomic configurations in large chemical phase spaces, it is not straightforward to describe structure–property relations in materials exhibiting configurational disorder. One example is iron-substituted strontium titanate (SrTi 1–x Fe x O 3–d , STF), a promising perovskite-derivative cathode material in solid oxide fuel cells that exhibits full solid solubility 0 ≤ x ≤ 1 and a tendency to exhibit short-range order. Here we demonstrate a multisublattice cluster expansion (CE) framework and apply it to STF across the full composition range. The CE approach is distinct from more traditional CE formulations in that clusters are defined explicitly by the chemical species distributed among multiple sublattices, rather than via cluster functions of occupation variables with decoration. The modified CE approach makes it easy to distinguish meaningful chemical interactions that are harder to extract from conventional CE, since for the latter chemical identity in a cluster is expressed as a product of site occupations. The least absolute shrinkage and selection operator (LASSO) is implemented as a regression analysis tool to select key clusters and avoid overfitting. We demonstrate this formulation on STF, and show that it can accurately predict configurational energies in comparison to conventional CE. From the key clusters, we identify that short-range ordering between substitutional Fe and oxygen vacancies (V O ) results in the formation of Fe–VO strings. In addition, we consider the stability of STF through CE-based Monte Carlo (MC) simulations and confirm the presence of superstructures that were previously observed in transmission electron microscopy. In this work, analysis of atomic configurations from MC samples reveals variations in the oxidation state of Fe atoms, which can be explained by the ordering tendency of Fe and V O . The cluster description and selection formalism described here may be applied to other disordered multisublattice systems for accurate and efficient material modeling.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Graph neural networks predict energetic and mechanical properties for models of solid solution metal alloy phases

Here, we developed a PyTorch-based architecture called HydraGNN that implements graph convolutional neural networks (GCNNs) to predict the formation energy and the bulk modulus for models of solid solution alloys for various atomic crystal structures and relaxed volumes. We trained the GCNN surrogate model on a dataset for nickel–niobium (NiNb) generated by the embedded atom model (EAM) empirical interatomic potential for demonstration purposes. The dataset was generated by calculating the formation energy and the bulk modulus as a prototypical elastic property for optimized geometries starting from initial body-centered cubic (BCC), face-centered cubic (FCC), and hexagonal compact packed (HCP) crystal structures, with configurations spanning the possible compositional range for each of the three types of initial crystal structures. Numerical results show that the GCNN model effectively predicts both the formation energy and the bulk modulus as function of the optimized crystal structure, relaxed volume, and configurational entropy of the model structures for solid solution alloys.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Low-cost thermal/environmental barrier coatings: A first-principles study

Development of low-cost advanced thermal/environmental barrier coating (T/EBC) materials with acceptable thermal and mechanical properties is essential for safeguarding ceramic composites substrate against thermal and chemical degradation, thereby enhancing the efficiency of components in the high-temperature section of gas turbine engines. To this end, here we employed density functional theory-based approaches to predict the thermodynamic, mechanical, and thermal properties of rare earth disilicates based on abundant rare earth elements, namely La 2 Si 2 O 7 and Ce 2 Si 2 O 7 , as potential alternatives to the current-state-of-the-art ytterbium disilicate EBCs that uses expensive and scarce element Yb. The present study predicts that G-phase Ce 2 Si 2 O 7 has an ultralow thermal conductivity (0.26 W/m/K at 1500 K) and the apparent bulk coefficient of thermal expansion (ABCTE) (≈6.9x10 -6 K -1 ) slightly higher than SiC, demonstrating great potential as low-cost high-performance T/EBC. However, La 2 Si 2 O 7 and Ce 2 Si 2 O 7 undergo an A- to G-phase polymorphic transition at around 1470 K, resulting in significant changes to crystal structure and lattice parameters, and accordingly CTE and lattice thermal conductivity.

36 MATERIALS SCIENCE↗
DOE OSTI2020

A novel fiber-fretting test for tribological characterization of the fiber/matrix interface

Ceramic matrix composites exhibit excellent high temperature properties and are candidate materials for structural applications from jet engines to nuclear fuel cladding. The strength and deformation behavior of these composites are intrinsically tied to the properties of the fiber/matrix interface. This research introduces a novel technique to evaluate the fundamental friction and wear characteristics of these interfaces. A case study on SiC f /PyC/SiC m composites applies in situ fiber fretting to investigate the kinetic friction coefficient and cyclic wear characteristics of the interphase. The experimental methodology is presented in explicit detail to encourage other researchers to explore the capabilities of this technique. Additionally, testing was carried out as a function of fiber roughness, PyC thickness (≈10, 500, 1200 nm), cycle count (10, 100, 1000), and frequency (1, 5, and 10 Hz). The friction coefficient and wear behavior changed with cycle length due to a transition from adhesive to abrasive mechanisms. SEM fractography provided insights to tribological evolution from adhesive sliding to three-body abrasive wear. The structural disorder at the tribo-surface relating to graphitic content and dangling bond density was investigated using Raman spectroscopy. Properties are contextualized with comparison to uniaxial tensile behavior of the parent composite.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Robust second-order approximation of the compressible Euler equations with an arbitrary equation of state

Here, this paper is concerned with the approximation of the compressible Euler equations supplemented with an arbitrary or tabulated equation of state. The proposed approximation technique is robust, formally second-order accurate in space, invariant-domain preserving, and works for every equation of state, tabulated or analytic, provided the pressure is nonnegative. An entropy surrogate functional that grows across shocks is proposed. The numerical method is verified with novel analytical solutions and then validated with several computational benchmarks seen in the literature including problems with composite waves.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Enhancing the accuracy and generality of the Debye–Grüneisen Model: Optimizing the volume dependence for accurate predictions across varied compositions

In this work, we have introduced an optimized Debye-Grüneisen model that revolutionizes the determination of the Debye temperature and Grüneisen parameters. Unlike conventional methods, our model requires only the 0 K energy volume data for a material as input, eliminating the need to determine the bulk modulus and its pressure derivative, which often pose challenges due to numerical uncertainties. This unique feature sets our model apart from existing approaches and streamlines the process, enabling accurate predictions of thermal expansion behavior across various materials. To demonstrate its effectiveness, we showcase its excellent agreement with measured coefficients of thermal expansion (CTE) for the nickel-cobalt-chromium-aluminum-yttrium (Ni-Co-Cr-Al-Y) bond-coating system. Additionally, we apply our approach by conducting a high-throughput search for potential bond-coating materials among 90,000 compositions within the aluminum-cobalt-chromium-iron-nickel (Al-Co-Cr-Fe-Ni) system. From this extensive search, four compositions are synthesized, and the measured CTE values agree very well with theoretical predictions, hence validating our approach. In conclusion, the current optimized Debye-Grüneisen model combined with Density Functional Theory (DFT)-based thermodynamic database enables reliable and efficient high-throughput calculations of CTE of of a material without expensive phonon calculations.

Bond coating materials↗
DOE OSTI2025

Single Crystals of Vanadium Oxides as a Lens for Understanding Structural and Electronic Phase Transformations, Ion Transport, Chemo-Mechanical Coupling, and Electrothermal Neuronal Emulation

Vanadium oxides cystallize in a diverse array of structures and compositions arising from the redox versatility of vanadium, variable covalency of V−O bonds, and myriad coordination geometries. Their open frameworks present abundant interstitial sites that enable insertion of guest-ions. In such compounds, V3d electron and spin localization and disorder couple strongly to structural preferences. The rich structural diversity manifests as a “rugged” free energy landscape with multiple interconvertible polymorphs. Such a landscape sets up structural, electronic, and magnetic transitions that underpin the promise of these materials as ion-insertion battery electrodes; compact primitives for brain-inspired computing, and heterogeneous catalysts. Here, we examine the structural and compositional diversity, electronic instabilities, defect dynamics, structure transformations, mechanical properties, and surface structure of vanadium oxides using single crystals as a distinctive lens. Single crystals enable the measurement of structure−function correlations without the ensemble and orientational averaging inevitable in polycrystalline materials. Their well-defined surfaces further enable examination of facet-dependent reactivity toward molecular adsorbates, ion fluxes, and lattice (mis)matched solids. We provide a comprehensive account of vanadium-oxide single-crystal studies, from delineation of common structural motifs to single-crystal growth techniques, topochemical modification strategies, mechanisms underpinning electronic instabilities, and implementation as electrothermal neurons and battery electrode materials.

Ponis, John [Texas A&M University, College Station↗